StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray images
This work addresses the need for more accurate AI-assisted diagnosis in medical imaging by leveraging multiple X-ray views, though it is incremental in its method.
The authors tackled the problem of classifying chest X-ray images by combining information from multiple views, achieving improved performance over single-view and traditional multi-view models on a dataset of 363,000 images.
Chest X-ray images are commonly used in medical diagnosis, and AI models have been developed to assist with the interpretation of these images. However, many of these models rely on information from a single view of the X-ray, while multiple views may be available. In this work, we propose a novel approach for combining information from multiple views to improve the performance of X-ray image classification. Our approach is based on the use of a convolutional neural network to extract feature maps from each view, followed by an attention mechanism implemented using a Vision Transformer. The resulting model is able to perform multi-label classification on 41 labels and outperforms both single-view models and traditional multi-view classification architectures. We demonstrate the effectiveness of our approach through experiments on a dataset of 363,000 X-ray images.